Normal Curves: Sexy Science, Serious Statistics

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn...

1 week ago

1 hour 29 minutes

Normal Curves: Sexy Science, Serious Statistics

The Batman Effect: Do weird surprises make people nicer?

Description

Nobody expects Batman—but when he shows up in a crowded subway car, are people suddenly more likely to help a passenger in need? This week on Normal Curves, we unpack a recent quasi-experimental field study involving a caped superhero costume, a prosthetic pregnancy belly, and some puzzled Italian commuters. Along the way, we demystify three common ways of describing effects for binary outcomes—risk differences, risk ratios, and odds ratios—and explain what they actually mean in plain language. We also do some statistical sleuthing, uncover a major problem hiding in the paper’s numbers, and debate what really counts as an effective Batman outfit.

Statistical topics

absolute vs relative effects
binary outcomes
coding errors
data errors and quality control
effect size interpretation
field experiments
odds
odds ratios
percentage differences
quasi-experimental studies
risk differences
risk ratios
statistical sleuthing

Methodological morals

“We love an uncluttered paper, but when it's missing the basics, it's like an empty fridge. Clean, yes, but dinner is not happening.”
“Before you make a fancy model, make sure the numbers in the table in the text match.”

References

Pagnini F, Grosso F, Cavalera C, et al. Unexpected events and prosocial behavior: the Batman effect. Npj Ment Health Res. 2025;4(1):57. Published 2025 Nov 3. doi:10.1038/s44184-025-00171-5
PubPeer. Comments on “Unexpected events and prosocial behavior: the Batman effect.” Accessed December 2025.
Sainani KL. Understanding odds ratios. PM R. 2011;3(3):263-267. doi:10.1016/j.pmrj.2011.01.009
Nuzzo RL. Communicating measures of relative risk in plain English. PM R. 2022;14(2):283-287. doi:10.1002/pmrj.12761
Sainani KL. How statistics can mislead. Am J Public Health. 2012;102:e3-4.

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(03:42) - Why would Batman make people nicer?
(07:33) - How they ran the experiment
(17:06) - Did Batman save the day? Different ways to answer that
(22:16) - What are odds and odds ratios?
(29:16) - Where people get it wrong
(34:08) - The plot twist: big numerical errors
(40:36) - Did men or women give up their seat more often?
(43:05) - Wrap-up and methodological morals

3 weeks ago

46 minutes

Normal Curves: Sexy Science, Serious Statistics

Holiday Survival Guide Part 2: The survey study edition

Does the temperature of your coffee six months ago really predict whether you feel gassy today? This week we dissect a new nutrition survey study on hot and cold beverage habits that claims to connect drink temperature with gut symptoms, anxiety, and more—despite relying on year-old memories and a blizzard of statistical tests. It’s the perfect case study for our Holiday Survival Guide Part 2, where we teach you how to talk with Uncle Joe at the dinner table about one of the most common—and most fraught—study designs in science: cross-sectional surveys. We walk through our easy checklist for making sense of results, show how recall bias and measurement error can skew the story, and reacquaint you with nonmonogamous Multiple-Testing Dude, who’s been very busy in this dataset. A friendly, practical guide to spotting when researchers are just torturing the data until it confesses.

Statistical topics

Confounding
Cross-sectional studies
False positives
Measurement error
Multiple testing
PICOT / PIVOT framework
Recall bias
Research hypotheses
Sample size and power
Signal vs. noise
SMART framework
Statistical significance
Subgroup analyses
Survey design
Transparency and trustworthiness

Methodological morals

“When your measurement starts with ‘think back to last winter’ you might as well use a random number generator.”
“If the effect is only significant in certain subgroups in certain seasons for certain outcomes, it might just be a bad case of gas.”

References

Wu T, Doyle C, Ito J, et al. Cold Exposures in Relation to Dysmenorrhea among Asian and White Women. Int J Environ Res Public Health. 2023;21(1):56. Published 2023 Dec 30. doi:10.3390/ijerph21010056
Wu T, Ramesh N, Doyle C, Hsu FC. Cold and hot consumption and health outcomes among US Asian and White populations. Br J Nutr. Published online September 18, 2025. doi:10.1017/S000711452510514X

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(04:36) - Did they have real research hypotheses?
(10:29) - Observational or randomized experiment?
(20:09) - PICOT and PIVOT
(26:20) - Memory problems
(32:03) - Five outcomes and measurement problems therein
(36:56) - SMART
(41:50) - Multiple Testing Dude is having a great time
(52:36) - How big is the effect?
(59:06) - Wrap-up and Irish Coffee rating scale

1 month ago

1 hour 4 minutes

Normal Curves: Sexy Science, Serious Statistics

Holiday Survival Guide: How to talk about scientific studies around the dinner table

Does a little alcohol really make you speak a foreign language better? This week we unpack a quirky randomized trial that tested Dutch pronunciation after a modest buzz—and came to the opposite conclusion the researchers expected. We use it as the perfect holiday case study: instead of arguing with Uncle Joe at the dinner table, we’ll show you how to pull apart a scientific headline using a friendly, practical checklist anyone can learn. Along the way we stress-test the study’s claims, take a quick detour into what a .04% buzz actually looks like, and run our own before-and-after experiment with two brave science journalists at the ScienceWriters2025 conference in Chicago. A holiday survival guide with vodka tonics, statistical sleuthing, and a few surprisingly smooth French phrases.

Statistical topics

Alternative explanations
Arithmetic consistency / GRIM test
Blinding
Effect size / magnitude
Generalizability / external validity
Observational studies vs. experiments
Outcome measurement
PICOT framework
Placebo and expectancy effects
Primary outcomes / pre-specification
Randomized controlled trials
Research hypotheses
Sample size
SMART framework
Statistical significance (signal vs. noise)
Transparency and trustworthiness

Methodological morals

“You don't need a PhD to read a study. Just remember, PICOT and SMART.”
“A decimal point can mean the difference between life and death. Details matter.”

References

Renner F, Kersbergen I, Field M, Werthmann J. Dutch courage? Effects of acute alcohol consumption on self-ratings and observer ratings of foreign language skills. J Psychopharmacol. 2018;32(1):116-122. doi:10.1177/0269881117735687

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

1 month ago

1 hour 1 minute

Normal Curves: Sexy Science, Serious Statistics

Shingles Shot and Dementia: Could one vaccine protect your brain?

What do chickenpox and shingles have to do with your brain? This week, we dig into two 2025 headline-grabbing studies that link the shingles shot to lower dementia rates. We start in Wales, where a birthday cutoff turned into the perfect natural experiment, and end in the U.S. with a multi-million-person megastudy. Featuring bias-variance Goldilockses, Fozzy-the-Bear regression discontinuities, a Barbie-versus-Oppenheimer showdown for propensity scores – and the hottest rebrand of inverse-probability weighting you’ll ever hear.

Statistical topics

Absolute vs. relative risk
Bias–variance tradeoff
Causal inference
Censoring
Confounding
Fuzzy regression discontinuity design
Healthy-user bias
Inverse probability of treatment weighting (IPTW)
Longitudinal study
Natural experiment
Negative controls
Optimal bandwidth
Propensity scores
Selection bias
Subgroup analysis
Triangular kernel weights

Methodological morals

“Propensity scores are the lipstick you put on observational pigs.”
“Natural experiments are a hot flirtation date with causality.”

References

Eyting M, Xie M, Michalik F, Heß S, Chung S, Geldsetzer P. A natural experiment on the effect of herpes zoster vaccination on dementia. Nature. 2025 May;641(8062):438-446. doi: 10.1038/s41586-025-08800-x. Epub 2025 Apr 2. PMID: 40175543; PMCID: PMC12058522.
Polisky V, Littmann M, Triastcyn A, et al. Varicella-zoster virus reactivation and the risk of dementia. Nat Med. Published online October 6, 2025. doi:10.1038/s41591-025-03972-5
Sainani KL. Propensity scores: uses and limitations. PM&R 2012; 4:693-97.

Detailed Show Notes Page

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro and first gratuitous mention of sex
(03:56) - What are shingles, chickenpox, and the vaccines against them?
(12:30) - Fun facts about the varicella zoster and herpes viruses
(17:16) - A natural experiment in Wales
(21:10) - What is the Goldilocks optimal bandwidth?
(25:33) - Fuzzy regression discontinuity design demystified
(31:59) - Shingles vaccine vs dementia showdown
(33:29) - Absolute risk reduction paradox
(37:00) - Effects for men and women differ
(39:48) - A giant longitudinal study
(46:32) - Propensity scores demystified via Barbie and Oppenheimer
(52:36) - Using propensity scores to make matches
(56:49) - Inverse probability of treatment weighting demystified via more Barbenheimer
(01:01:08) - Attempts to rename IPTW for TikTok
(01:04:40) - Longitudinal study results
(01:08:41) - Smooch ratings and methodological morals: pigs and hot dates

2 months ago

1 hour 11 minutes

Normal Curves: Sexy Science, Serious Statistics

Scary Bridge Study: Can fear make you horny?

What if a haunted house makes your date look hotter? This week we dive into the infamous Scary Bridge Study — the 1970s classic that launched a thousand pop-psych takes on fear and lust. It’s the one with the swaying bridge, pretty “research assistant,” and phone number scrawled on torn paper. The study became legend, but how sturdy were its stats? We retrace the design, redo the numbers, and see how many math errors it takes to sway a suspension bridge. Along the way we find an erotic-fiction writing exercise, Adventure Dudes choosing their own experimental groups, and snarky replicators who tried (and failed) to make fear sexy again. We wrap with what the latest research says about when fear really does boost attraction — and when it backfires spectacularly. A Halloween story of danger, desire, and unconscious sexual drive.

This episode has a video version! https://www.youtube.com/watch?v=2coWoS_3460

Statistical topics

Arithmetic checks
Chi-square test
Confounders
GRIM test
Inter-rater reliability
Meta-analysis
Negative control
Randomization
Replication
Sample size
Signal vs. noise
Statistical sleuthing
Subjective measurement
T-test

Methodological morals

“Those who don't verify their numbers dig their own statistical graves.”
“Famous doesn't mean flawless.”

References

Brown, NJ, Heathers, JA. The GRIM test: A simple technique detects numerous anomalies in the reporting of results in psychology. Social Psychological and Personality Science. 2017; 8(4):363-369.
Dutton DG, Aron AP. Some evidence for heightened sexual attraction under conditions of high anxiety. J Pers Soc Psychol. 1974;30(4):510-517. doi:10.1037/h0037031
Foster CA, Witcher BS, Campbell WK, Green JD. Arousal and attraction: Evidence for automatic and controlled processes. J Pers Soc Psychol. 1998;74(1):86-101.
Kenrick DT, Cialdini R, Linder D. Misattribution under fear-producing circumstances: Four failures to replicate. Pers Soc Psychol Bull. 1979;5(3):329-334.
van der Zee T, Anaya J, Brown NJL. Statistical heartburn: an attempt to digest four pizza publications from the Cornell Food and Brand Lab. BMC Nutr. 2017;3:54. Published 2017 Jul 10. doi:10.1186/s40795-017-0167-x
http://www.prepubmed.org/grim_test/

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro: Fear and Flirtation on a Suspension Bridge
(05:40) - A Classic 1970s Experiment with No IRB to be Found
(11:15) - Adventure Dudes Choose Their Own Bridge
(17:00) - The Sexy Story Scale
(22:20) - Cool Factor and the Negative Control
(28:54) - Grim Reaper Math
(36:29) - T-Tests, Chi-Squares, and Shaky Results
(42:44) - Electric Shocks and Damsels in Distress
(50:49) - Replications and Rejections
(58:39) - Wrap-Up, Methodological Morals, and a New Sexy Rating Scale

2 months ago

1 hour 4 minutes

Normal Curves: Sexy Science, Serious Statistics

Ultramarathons: Can vitamin D protect your bones?

Ultramarathoners push their bodies to the limit, but can a giant pre-race dose of vitamin D really keep their bones from breaking down? In this episode, we dig into a trial that tested this claim – and found a statistical endurance event of its own: six highly interchangeable papers sliced from one small study. Expect missing runners, recycled figures, and a peer-review that reads like stand-up comedy, plus a quick lesson in using degrees of freedom as your statistical breadcrumbs.

Statistical topics

Data cleaning and validation
Degrees of freedom
Exploratory vs confirmatory analysis
False positives and Type I error
Intention-to-treat principle
Multiple testing
Open data and transparency
P-hacking
Salami slicing
Parametric vs non-parametric tests
Peer review quality
Randomized controlled trials
Research reproducibility
Statistical sleuthing

Methodological morals

“Degrees of freedom are the breadcrumbs in statistical sleuthing. They reveal the sample size even when the authors do not.”
“Publishing the same study again and again with only the outcomes swapped is Mad Libs Science, better known as salami slicing.”

References

Boswell, Rachel. Pre-race vitamin D could do wonders for ultrarunners’ bone health, according to science. Runner’s World. September 25, 2025.
Mieszkowski J, Stankiewicz B, Kochanowicz A, et al. Ultra-Marathon-Induced Increase in Serum Levels of Vitamin D Metabolites: A Double-Blind Randomized Controlled Trial. Nutrients. 2020;12(12):3629. Published 2020 Nov 25. doi:10.3390/nu12123629
Mieszkowski J, Borkowska A, Stankiewicz B, et al. Single High-Dose Vitamin D Supplementation as an Approach for Reducing Ultramarathon-Induced Inflammation: A Double-Blind Randomized Controlled Trial. Nutrients. 2021;13(4):1280. Published 2021 Apr 13. doi:10.3390/nu13041280
Mieszkowski J, Brzezińska P, Stankiewicz B, et al. Direct Effects of Vitamin D Supplementation on Ultramarathon-Induced Changes in Kynurenine Metabolism. Nutrients. 2022;14(21):4485. Published 2022 Oct 25. doi:10.3390/nu14214485
Mieszkowski J, Brzezińska P, Stankiewicz B, et al. Vitamin D Supplementation Influences Ultramarathon-Induced Changes in Serum Amino Acid Levels, Tryptophan/Branched-Chain Amino Acid Ratio, and Arginine/Asymmetric Dimethylarginine Ratio. Nutrients. 2023;15(16):3536. Published 2023 Aug 11. doi:10.3390/nu15163536
Stankiewicz B, Mieszkowski J, Kochanowicz A, et al. Effect of Single High-Dose Vitamin D3 Supplementation on Post-Ultra Mountain Running Heart Damage and Iron Metabolism Changes: A Double-Blind Randomized Controlled Trial. Nutrients. 2024;16(15):2479. Published 2024 Jul 31. doi:10.3390/nu16152479
Stankiewicz B, Kochanowicz A, et al. Single high-dose vitamin D supplementation impacts ultramarathon-induced changes in serum levels of bone turnover markers: a double-blind randomized controlled trial. J Int Soc Sports Nutr. 2025 Dec;22(1):2561661. doi: 10.1080/15502783.2025.2561661.

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

00:00 Intro & claim of the episode
00:44 Runner’s World headline: Vitamin D for ultramarathoners
02:03 Kristin’s connection to running and vitamin D skepticism
03:32 Ultramarathon world—Regina’s stories and Death Valley race
06:29 What ultramarathons do to your bones
08:02 Boy story: four stress fractures in one race
10:00 Study design—40 male runners in Poland
11:33 Missing flow diagram and violated intention-to-treat
13:02 The intervention: 150,000 IU megadose
15:09 Blinding details and missing randomization info
17:13 Measuring bone biomarkers—no primary outcome specified
19:12 The wrong clinicaltrials.gov registration
20:35 Discovery of six papers from one dataset (salami slicing)
23:02 Why salami slicing misleads readers
25:42 Inconsistent reporting across papers
29:11 Changing inclusion criteria and sloppy methods
31:06 Typos, Polish notes, and misnumbered references
32:39 Peer review comedy gold—“Please define vitamin D”
36:06 Reviewer laziness and p-hacking admission
39:13 Results: implausible bone growth mid-race
41:16 Degrees of freedom sleuthing reveals hidden sample sizes
47:07 Open data? Kristin emails the authors
48:42 Lessons from Kristin’s own ultramarathon dataset
51:22 Fishing expeditions and misuse of parametric tests
53:07 Strength of evidence: one smooch each
54:44 Methodologic morals—Mad Libs Science & degrees of freedom breadcrumbs
56:12 Anyone can spot red flags—trust your eyes
57:34 Outro: skip the vitamin D shot before your next run

3 months ago

58 minutes

Normal Curves: Sexy Science, Serious Statistics

P-Values: Are we using a flawed statistical tool?

P-values show up in almost every scientific paper, yet they’re one of the most misunderstood ideas in statistics. In this episode, we break from our usual journal-club format to unpack what a p-value really is, why researchers have fought about it for a century, and how that famous 0.05 cutoff became enshrined in science. Along the way, we share stories from our own papers—from a Nature feature that helped reshape the debate to a statistical sleuthing project that uncovered a faulty method in sports science. The result: a behind-the-scenes look at how one statistical tool has shaped the culture of science itself.

Statistical topics

Bayesian statistics
Confidence intervals
Effect size vs. statistical significance
Fisher’s conception of p-values
Frequentist perspective
Magnitude-Based Inference (MBI)
Multiple testing / multiple comparisons
Neyman-Pearson hypothesis testing framework
P-hacking
Posterior probabilities
Preregistration and registered reports
Prior probabilities
P-values
Researcher degrees of freedom
Significance thresholds (p < 0.05)
Simulation-based inference
Statistical power
Statistical significance
Transparency in research
Type I error (false positive)
Type II error (false negative)
Winner’s Curse

Methodological morals

“If p-values tell us the probability the null is true, then octopuses are psychic.”
“Statistical tools don't fool us, blind faith in them does.”

References

Nuzzo R. Scientific method: statistical errors. Nature. 2014 Feb 13;506(7487):150-2. doi: 10.1038/506150a.
Nuzzo, R., 2015. Scientists perturbed by loss of stat tools to sift research fudge from fact. Scientific American, pp.16-18.
Nuzzo RL. The inverse fallacy and interpreting P values. PM&R. 2015 Mar;7(3):311-4. doi: 10.1016/j.pmrj.2015.02.011. Epub 2015 Feb 25.
Nuzzo, R., 2015. Probability wars. New Scientist, 225(3012), pp.38-41.
Sainani KL. Putting P values in perspective. PM&R. 2009 Sep;1(9):873-7. doi: 10.1016/j.pmrj.2009.07.003.
Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4(6):442-5. doi: 10.1016/j.pmrj.2012.04.014.
McLaughlin MJ, Sainani KL. Bonferroni, Holm, and Hochberg corrections: fun names, serious changes to p values. PM&R. 2014 Jun;6(6):544-6. doi: 10.1016/j.pmrj.2014.04.006. Epub 2014 Apr 22.
Sainani KL. The Problem with "Magnitude-based Inference". Med Sci Sports Exerc. 2018 Oct;50(10):2166-2176. doi: 10.1249/MSS.0000000000001645.
Sainani KL, Lohse KR, Jones PR, Vickers A. Magnitude-based Inference is not Bayesian and is not a valid method of inference. Scand J Med Sci Sports. 2019 Sep;29(9):1428-1436. doi: 10.1111/sms.13491.
Lohse KR, Sainani KL, Taylor JA, Butson ML, Knight EJ, Vickers AJ. Systematic review of the use of "magnitude-based inference" in sports science and medicine. PLoS One. 2020 Jun 26;15(6):e0235318. doi: 10.1371/journal.pone.0235318.
Wasserstein, R.L. and Lazar, N.A., 2016. The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), pp.129-133.

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro & claim of the episode
(01:00) - Why p-values matter in science
(02:44) - What is a p-value? (ESP guessing game)
(06:47) - Big vs. small p-values (psychic octopus example)
(08:29) - Significance thresholds and the 0.05 rule
(09:00) - Regina’s Nature paper on p-values
(11:32) - Misconceptions about p-values
(13:18) - Fisher vs. Neyman-Pearson (history & feud)
(16:26) - Botox analogy and type I vs. type II errors
(19:41) - Dating app analogies for false positives/negatives
(22:02) - How the 0.05 cutoff got enshrined
(23:46) - Misinterpretations: statistical vs. practical significance
(25:22) - Effect size, sample size, and “statistically discernible”
(25:51) - P-hacking and researcher degrees of freedom
(28:52) - Transparency, preregistration, and open science
(29:58) - The 0.05 cutoff trap (p = 0.049 vs 0.051)
(30:24) - The biggest misinterpretation: what p-values actually mean
(32:35) - Paul the psychic octopus (worked example)
(35:05) - Why Bayesian statistics differ
(38:55) - Why aren’t we all Bayesian? (probability wars)
(40:11) - The ASA p-value statement (behind the scenes)
(42:22) - Key principles from the ASA white paper
(43:21) - Wrapping up Regina’s paper
(44:39) - Kristin’s paper on sports science (MBI)
(47:16) - What MBI is and how it spread
(49:49) - How Kristin got pulled in (Christie Aschwanden & FiveThirtyEight)
(53:11) - Critiques of MBI and “Bayesian monster” rebuttal
(55:20) - Spreadsheet autopsies (Welsh & Knight)
(57:11) - Cherry juice example (why MBI misleads)
(59:28) - Rebuttals and smoke & mirrors from MBI advocates
(01:02:01) - Winner’s Curse and small samples
(01:02:44) - Twitter fights & “establishment statistician”
(01:05:02) - Cult-like following & Matrix red pill analogy
(01:07:12) - Wrap-up

3 months ago

1 hour 13 minutes

Normal Curves: Sexy Science, Serious Statistics

Exercise and Cancer: Does physical activity improve colon cancer survival?

Exercise has long been hailed as cancer-fighting magic, but is there hard evidence behind the hype? In this episode, we tackle the CHALLENGE trial, a large phase III study of colon cancer patients that tested whether prescribed exercise could improve cancer-free survival. We translate clinical jargon into plain English, show why ratio statistics make splashy headlines while absolute differences tell the real story, and take a detour into why statisticians think survival analysis is downright sexy. And we even bring in a classic reality show to make sense of the numbers.

Statistical topics

Data and Safety Monitoring Board (DSMB)
Hazard ratios
Intention-to-treat analysis
Interim analyses
Kaplan-Meier curves
Phase III trials
Randomized clinical trial
Rates and rate ratios
Relative vs absolute differences
Stratified randomization with minimization
Survival analysis
Time-to-event variables

Methodological morals

“Ratio statistics sell headlines. Absolute differences sell truth.”
“Survival analysis is this sexy stats tool that makes every moment and every Cox count.”

References

Courneya KS, Vardy JL, O'Callaghan CJ, et al. Structured Exercise after Adjuvant Chemotherapy for Colon Cancer. NEJM. 2025;393:13-25.
Rabin RC. Are Marathons and Extreme Running Linked to Colon Cancer? The New York Times. Aug 19, 2025.
Sainani KL. Introduction to survival analysis. PM&R. 2016; 8:580-85.
Sainani KL. Making sense of intention-to-treat. PM&R. 2010;2:209-13.

Thanks

Thanks to Caitlin Goodrich for the episode topic tip!

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(05:42) - Two different types of cancer studies
(08:12) - Why might exercise affect cancer?
(10:05) - Phase III trials are different
(12:40) - Who was in the CHALLENGE trial?
(13:31) - Stratified randomization with minimization
(15:05) - The exercise prescription
(18:23) - What did the CHALLENGE trial measure?
(19:10) - Disease-free survival
(21:05) - Data and Safety Monitoring Board – what do they do?
(23:41) - Participants and adherence to exercise
(26:00) - Intention-to-treat analysis
(29:04) - Survival analysis overview
(30:57) - Kaplan-Meier curves
(33:33) - Reality-show analogy
(36:00) - Ratio statistics are confusing
(38:36) - Hazard ratios
(46:09) - Wrap-up, rating, and methodological morals

4 months ago

49 minutes

Normal Curves: Sexy Science, Serious Statistics

Age Gaps: How much does age matter in dating?

Are we all secretly ageist when it comes to dating? We put the stereotype that older men prefer younger women under the microscope using data from thousands of blind dates. What we found surprised us: the “age penalty” was real but microscopic, women wanted younger partners too, and hard age cutoffs weren’t so hard after all. Along the way, we unpack statistical significance versus practical importance, play with the infamous “half your age plus seven” rule, and imagine what it would take for love to die out… somewhere around age 628.

Statistical topics

Discontinuous regression
Effect sizes
Extrapolation pitfalls
Linear regression
Logistic regression
Odds ratios
Open data
Statistical significance vs. practical significance

Methodological morals

“Do not be swept off your feet by statistical significance. Tiny effects in bed are still tiny.”
“Fancy units sound smart, but plain English wins hearts.”

Show Notes Technical Appendix (with step-by-step explanations)

References

Eastwick PW, Finkel EJ, Meza EM, Ammerman K. No gender differences in attraction to young partners: A study of 4500 blind dates. Proc Natl Acad Sci U S A. 2025 Feb 4;122(5):e2416984122.
Matchmaking Dataset and Code on Open Science Framework: https://osf.io/rkm2d/?view_only=a0fe91dae0464077af7772e6890a8151
Nuzzo RL. Communicating measures of relative risk in plain English. PM&R. 2022 Feb;14(2):283-7.
O'Rell, Max. Her Royal Highness, Woman: And His Majesty--Cupid. Abbey Press, 1901.
Sainani KL. Logistic regression. PM&R. 2014 Dec;6(12):1157-62.
Sainani KL. Understanding odds ratios. PM&R. 2011 Mar;3:263-7.
Sainani KL. Clinical versus statistical significance. PM&R. 2012 Jun;4:442-5.

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(04:01) - Half-your-age-plus-seven rule
(09:15) - Matchmaking service for the study
(17:05) - Blind dates as natural experiments
(21:55) - Regression results part 1: Age penalties?
(28:38) - Wait, how big of an effect was that?
(34:09) - Odds ratio of a second date
(38:01) - Surprising age pair-ups
(40:53) - Regression results part 2: Deal-breaking age limits?
(44:27) - Why the patterns may or may not be true
(46:30) - Wrap-up, ratings, and methodological morals

4 months ago

49 minutes

Normal Curves: Sexy Science, Serious Statistics

Your Brain on AI: Is ChatGPT making us mentally lazy?

ChatGPT is melting our brainpower, killing creativity, and making us soulless — or so the headlines imply. We dig into the study behind the claims, starting with quirky bar charts and mysterious sample sizes, then winding through hairball-like brain diagrams and tens of thousands of statistical tests. Our statistical sleuthing leaves us with questions, not just about the results, but about whether this was science’s version of a first date that looked better on paper.

Statistical topics

ANOVA
Bar graphs
Data visualization
False Discovery Rate correction
Multiple testing
Preprints
Statistical Sleuthing

Methodological morals

"Treat your preprints like your blind dates. Show up showered and with teeth brushed."
"Always check your N. Then check it again."
"Never make a bar graph that just shows p-values. Ever."

Link to paper

Kristin and Regina’s online courses:

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(03:46) - Media coverage of the study
(08:35) - The experiment
(12:09) - Sample size issues
(13:11) - Bar chart sleuthing
(19:15) - Blind date analogy
(22:57) - Interview results
(29:07) - Simple text analysis results
(33:07) - Natural language processing results
(40:03) - N-gram and ontology analysis results
(44:58) - Teacher evaluation results
(51:33) - Neuroimaging analysis
(59:35) - Multiple testing and connectivity issues
(01:05:13) - Brain adaptation results
(01:08:50) - Wrap-up, rating, and methodological morals

4 months ago

1 hour 14 minutes

Normal Curves: Sexy Science, Serious Statistics

The Backfire Effect: Can fact-checking make false beliefs stronger?

Can correcting misinformation make it worse? The “backfire effect” claims that debunking myths can actually make false beliefs stronger. We dig into the evidence — from ghost studies to headline-making experiments — to see if this psychological plot twist really holds up. Along the way, we unpack interaction effects, randomization red flags, and what happens when bad citations take on a life of their own. Plus: dirty talk analogies, statistical sleuthing, and why “familiarity” might be your brain’s sneakiest trick.

Statistical topics

Computational replication
Replication
Block randomization
Problems in randomization
Bad citing
Interactions in regression

Unpublished "Ghost Paper"

PDF retrieved from the Wayback Machine

Citations

Nyhan B, Reifler J. When corrections fail: The persistence of political misperceptions. Political Behavior. 2010;32:303–330.
Skurnik I, Yoon C, Schwarz N. “Myths & Facts” about the flu: Health education campaigns can reduce vaccination intentions. Unpublished manuscript, PDF posted separately.
Schwarz N, Sanna LJ, Skurnik I, et al. Metacognitive experiences and the intricacies of setting people straight: Implications for debiasing and public information campaigns. Advances in Experimental Social Psychology. 2007;39:127–61.
Lewandowsky S, Ecker UKH, Seifert CM, et al. Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest. 2012;13:106–131.
Pluviano S, Watt C, Della Sala S. Misinformation lingers in memory: Failure of three pro-vaccination strategies. PLOS ONE. 2017;12:e0181640.
Pluviano S, Watt C, Ragazzini G, et al. Parents’ beliefs in misinformation about vaccines are strengthened by pro‑vaccine campaigns. Cognitive Processing. 2019;20:325–31.
Wood T, Porter E. The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior. 2019;41:135–63.
Nyhan B, Porter E, Reifler J, Wood TJ. Taking fact-checks literally but not seriously? The effects of journalistic fact-checking on factual beliefs and candidate favorability. Political Behavior. 2020;42:939–60.
Ecker UKH, Hogan JL, Lewandowsky S. Reminders and repetition of misinformation: Helping or hindering its retraction? Journal of Applied Research in Memory and Cognition. 2017;6:185–92.
Swire B, Ecker UKH, Lewandowsky S. The role of familiarity in correcting inaccurate information. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2017;43:1948–61.
Ecker UKH, O’Donnell M, Ang LC, et al. The effectiveness of short- and long-format retractions on misinformation belief and recall. British Journal of Psychology. 2020;111:36–54.
Ecker UKH, Sharkey CXM, Swire-Thompson B. Correcting vaccine misinformation: A failure to replicate familiarity or fear-driven backfire effects. PLOS ONE. 2023;18:e0281140.
Cook J, Lewandowsky S. The Debunking Handbook. University of Queensland. 2011.
Lewandowsky S, Cook J, Ecker UKH, et al. The Debunking Handbook 2020. Available at https://sks.to/db2020.
Swire‑Thompson B, DeGutis J, Lazer D. Searching for the backfire effect: Measurement and design considerations. Journal of Applied Research in Memory and Cognition. 2020;9:286–99.

Kristin and Regina’s online courses:

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) -
(00:00) - Intro
(02:05) - What is the backfire effect?
(03:55) - The 2010 paper that panicked fact-checkers
(06:25) - The ghost paper what it really said
(12:35) - Study design of the 2010 paper
(18:25) - Results of the 2010 paper
(19:55) - Crossover interactions, regression models, and intimate talk
(25:24) - Missing data and cleaning your bedroom analogy
(28:11) - Fact-checking the fact-checking paper
(33:07) - Replication and pushing the data to the limit
(36:59) - The purported backfire effect spreads
(41:06) - The 2017 paper that got a lot of attention
(44:25) - Statistical sleuthing the 2017 paper
(48:51) - Will researchers double down on their earlier conclusions?
(54:46) - A review paper sums it all up
(56:00) - Wrap up, rating, and methodological morals

5 months ago

58 minutes

Normal Curves: Sexy Science, Serious Statistics

Dating Wishlists: Are we happier when we get what we want in a mate?

Loyal, funny, hot — you’ve probably got a wish list for your dream partner. But does checking all your boxes actually lead to happily ever after? In this episode, we dive into a massive global study that put the “ideal partner” hypothesis to the test. Do people really know what they want, and does getting it actually make them happier? We explore surprising statistical insights from over 10,000 romantics in 43 countries, from mean-centering and interaction effects to the good-catch confounder. Along the way, we dig into dessert metaphors, partner boat-count regression models, and the one trait that people say doesn’t matter — but secretly makes them happiest.

Statistical topics

Regression
Random Slopes and Intercepts (Random Effects) in Regression
Standardized Beta Coefficients in Regression
Interaction Effects in Regression
Mean Centering
Exploratory Analyses

Methodological morals

“Good science bares it all.”

“When the world isn't one size fits all, don't fit just one line; use random slopes and intercepts.”

References

Eastwick PW, Sparks J, Finkel EJ, Meza EM, Adamkovič M, Adu P, Ai T, Akintola AA, Al-Shawaf L, Apriliawati D, Arriaga P, Aubert-Teillaud B, Baník G, Barzykowski K, Batres C, Baucom KJ, Beaulieu EZ, Behnke M, Butcher N, Charles DY, Chen JM, Cheon JE, Chittham P, Chwiłkowska P, Cong CW, Copping LT, Corral-Frias NS, Ćubela Adorić V, Dizon M, Du H, Ehinmowo MI, Escribano DA, Espinosa NM, Expósito F, Feldman G, Freitag R, Frias Armenta M, Gallyamova A, Gillath O, Gjoneska B, Gkinopoulos T, Grafe F, Grigoryev D, Groyecka-Bernard A, Gunaydin G, Ilustrisimo R, Impett E, Kačmár P, Kim YH, Kocur M, Kowal M, Krishna M, Labor PD, Lu JG, Lucas MY, Małecki WP, Malinakova K, Meißner S, Meier Z, Misiak M, Muise A, Novak L, O J, Özdoğru AA, Park HG, Paruzel M, Pavlović Z, Püski M, Ribeiro G, Roberts SC, Röer JP, Ropovik I, Ross RM, Sakman E, Salvador CE, Selcuk E, Skakoon-Sparling S, Sorokowska A, Sorokowski P, Spasovski O, Stanton SCE, Stewart SLK, Swami V, Szaszi B, Takashima K, Tavel P, Tejada J, Tu E, Tuominen J, Vaidis D, Vally Z, Vaughn LA, Villanueva-Moya L, Wisnuwardhani D, Yamada Y, Yonemitsu F, Žídková R, Živná K, Coles NA. A worldwide test of the predictive validity of ideal partner preference matching. J Pers Soc Psychol. 2025 Jan;128(1):123-146. doi: 10.1037/pspp0000524
Love Factually Podcast: https://www.lovefactuallypod.com/

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) -
(00:00) - Intro
(04:57) - Actual dating profile wishlists vs study wishlists
(09:12) - Juicy paper details
(18:31) - What the study actually asked – wishlist, partner resume, relationship satisfaction
(24:10) - Linear regression illustrated through number of boats your partner has
(30:37) - Standardized regression coefficients illustrated through spouse height concordance
(34:52) - Good catch confounder: We all just want the same high-quality ice cream / mate
(39:46) - Does your personalized wishlist matter? Results
(42:01) - Wishlist regression interaction effects: like chocolate and peanut butter
(45:51) - Partner traits result in happiness bonus points
(49:51) - What do we say we want – and what really makes us happy? Surprise
(54:10) - Gender stereotypes and whether they held up
(56:51) - Random effects models and boats again
(59:30) - Other cool things they did
(01:00:41) - One-minute paper summary
(01:02:23) - Wrap-up, rate the claim, methodological morals

5 months ago

1 hour 5 minutes

Normal Curves: Sexy Science, Serious Statistics

Stats Reunion: What have we learned so far?

It’s our first stats reunion! In this special review episode, we revisit favorite concepts from past episodes—p-values, multiple testing, regression adjustment—and give them fresh personalities as characters. Meet the seductive false positive, the clingy post hoc ex, and Charlotte, the well-meaning but overfitting idealist.

Statistical topics

Bar charts vs Box plots
Bonferroni correction
Confounding
False positives
Multiple testing
Multivariable regression
Outcome switching
Over-adjustment
Post hoc analysis
Pre-registration
Residual confounding
Statistical adjustment using regression
Subgroup analysis
Unmeasured confounding

Review Sheet

References

Nuzzo RL. The Box Plots Alternative for Visualizing Quantitative Data. PM R. 2016 Mar;8(3):268-72. doi: 10.1016/j.pmrj.2016.02.001. Epub 2016 Feb 15. PMID: 26892802.
Sainani KL. The problem of multiple testing. PM R. 2009 Dec;1(12):1098-103. doi: 10.1016/j.pmrj.2009.10.004. PMID: 20006317.

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Programs that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Intro
(02:26) - Mailbag
(06:42) - P-values
(12:43) - Multiple Testing Guy
(16:05) - Bonferroni solution
(17:11) - Post hoc analysis ex
(22:22) - Subgroup analysis person
(29:34) - Statistical adjustment idealist
(43:00) - Unmeasured confounding
(44:25) - Residual confounding
(48:31) - Over-adjustment
(53:48) - Wrap-up

6 months ago

56 minutes

Normal Curves: Sexy Science, Serious Statistics

HPV Vaccine: How close are we to wiping out cervical cancer?

Could a preteen vaccine wipe out a global cancer? In this episode, we examine the bold claim that cervical cancer could be eradicated in much of the world by the end of the century—thanks to the highly effective HPV vaccine. We unpack statistical modeling, microsimulations, and how Markov chains make good date-night conversation. We also explore why vaccine uptake has been uneven, how a splash of vinegar is helping screen for cancer in low-resource countries, and why HPV isn’t just a women’s issue—it now causes more cancer in men than in women. Plus: dangerously tight corsets, allegedly breast-squeezing nuns, and the Cosmo quote we wish we’d written ourselves.

Statistical topics:

Cancer surveillance
Markov models
Microsimulation models
Sensitivity analyses
Passive surveillance
Background rates
Case reports and case series

Methodologic morals:

“When reality is too complex to test, let microsimulations do the rest.”
“Case reports are medicine's equivalent to see something, say something. They call for hard data, not hysteria.”

Citations:

No cervical cancer cases detected in vaccinated women following HPV immunisation. University of Strathclyde, January 22, 2024.
Palmer TJ, Kavanagh K, Cuschieri K, et al. Invasive cervical cancer incidence following bivalent human papillomavirus vaccination: a population-based observational study of age at immunization, dose, and deprivation. J Natl Cancer Inst. 2024;116:857-65.
Rigoni-Stern. Statistical facts about cancers on which Doctor Rigoni-Stern based his contribution to the Surgeons' Subgroup of the IV Congress of the Italian Scientists on 23 September 1842. (translation). Stat Med. 1987;6:881-4.
Gordan JA, Lenkei SC. Cleanliness, Continence, Constancy, and Cervical Carcinoma. Can Med Assoc J. 1964;90:1132.
zur Hausen H. Condylomata acuminata and human genital cancer. Cancer Res. 1976;36:794.
Walboomers JM, Jacobs MV, Manos MM, et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 1999;189:12-9.
Chesson HW, Dunne EF, Hariri S, et al. The estimated lifetime probability of acquiring human papillomavirus in the United States. Sex Transm Dis. 2014;41:660-4.
Sullivan, Morgan. Let’s Have a Little Chat About the HPV Vaccine. Cosmopolitan. March 19, 2025.
Burger EA, Kim JJ, Sy S, et al. Age of Acquiring Causal Human Papillomavirus (HPV) Infections: Leveraging Simulation Models to Explore the Natural History of HPV-induced Cervical Cancer. Clin Infect Dis. 2017;65:893-99.
Canfell K. Towards the global elimination of cervical cancer. Papillomavirus Res. 2019;8:100170.
World Health Organization. Global strategy to accelerate the elimination of cervical cancer as a public health problem. November 17, 2020.
Hall MT, Simms KT, Lew JB, et al. The projected timeframe until cervical cancer elimination in Australia: a modelling study. Lancet Public Health. 2019;4:e19-e27.
Burger EA, Smith MA, Killen J, et al. Projected time to elimination of cervical cancer in the USA: a comparative modelling study. Lancet Public Health. 2020 Apr;5(4):e213-e222.
Brisson M, Kim JJ, Canfell K, et al. Impact of HPV vaccination and cervical screening on cervical cancer elimination: a comparative modelling analysis in 78 low-income and lower-middle-income countries. Lancet. 2020;395:575-90.
Escabí-Wojna E, Alvelo-Fernández PM, Suárez E, et al. Sex differences in parental reasons for lack of intent to initiate HPV vaccination among adolescents ages 13-17 years: National Immunization Survey - Teen 2019-2021. Vaccine. 2025;44:126584. (see supplement)
Szilagyi PG, Albertin CS, Gurfinkel D, et al. Prevalence and characteristics of HPV vaccine hesitancy among parents of adolescents across the US. Vaccine. 2020;38:6027-6037.
LaPook, Jonathan. Is the HPV Vaccine Safe? CBS Evening News. August 18, 2009.
Slade BA, Leidel L, Vellozzi C, et al. Postlicensure safety surveillance for quadrivalent human papillomavirus recombinant vaccine. JAMA. 2009;302:750-7.
Kharabsheh S, Al-Otoum H, Clements J, et al. Mass psychogenic illness following tetanus-diphtheria toxoid vaccination in Jordan. Bull World Health Organ. 2001;79:764-70.
Jones TF, Craig AS, Hoy D, et al. Mass psychogenic illness attributed to toxic exposure at a high school. N Engl J Med. 2000;342:96-100.
Buttery JP, Madin S, Crawford NW, et al. Mass psychogenic response to human papillomavirus vaccination. Med J Aust. 2008;189:261-2.
Clements CJ. Gardasil and mass psychogenic illness. Aust N Z J Public Health. 2007;31:387.
Simas C, Munoz N, Arregoces L, et al. HPV vaccine confidence and cases of mass psychogenic illness following immunization in Carmen de Bolivar, Colombia. Hum Vaccin Immunother. 2019;15:163-66.
Larson HJ. Japan's HPV vaccine crisis: act now to avert cervical cancer cases and deaths. Lancet Public Health. 2020;5:e184-e185.
Brinth LS, Pors K, Theibel AC, Mehlsen J. Orthostatic intolerance and postural tachycardia syndrome as suspected adverse effects of vaccination against human papilloma virus. Vaccine. 2015;33:2602-5.
Large well-done studies following up on case reports and passive surveillance:
Phillips A, Hickie M, Totterdell J, Brotherton J, Dey A, Hill R, Snelling T, Macartney K. Adverse events following HPV vaccination: 11 years of surveillance in Australia. Vaccine. 2020;38:6038-46.
Arnheim-Dahlström L, Pasternak B, Svanström H, et al.

6 months ago

1 hour 15 minutes

Normal Curves: Sexy Science, Serious Statistics

Equipment Size: What is average?

Today’s deep dive: the surprisingly serious science of penis size. Using self-report surveys, objective measurements, and a healthy dose of old-school statistics, we ask: How do you get clean data on gentlemen’s goods?Along the way, we explore social desirability bias, survey design tricks, and what happens when science meets insecurity. You’ll never look at a Starbucks cup the same way again.

Statistical topics

Social desirability bias
Selection bias
Volunteer Bias
Descriptive Statistics
Right-Skewed Distributions
Strategies to improve accuracy in self-report data

Methodological morals

“When answers aim to please, truth takes its leave.”
“Without descriptive statistics, you'll never know if you measure up.”

References

CROWNE DP, MARLOWE D. A new scale of social desirability independent of psychopathology. J Consult Psychol. 1960;24:349-354. doi:10.1037/h0047358
Gebhard, P.H. and Johnson, A.B., 1998. The Kinsey data: Marginal tabulations of the 1938-1963 interviews conducted by the Institute for Sex Research. Indiana University Press.
Herbenick D, Reece M, Schick V, Sanders SA. Erect penile length and circumference dimensions of 1,661 sexually active men in the United States. J Sex Med. 2014;11(1):93-101. doi:10.1111/jsm.12244
Johnston, L., McLellan, T., & McKinlay, A. (2014). (Perceived) size really does matter: Male dissatisfaction with penis size. Psychology of Men & Masculinity, 15(2), 225–228. https://doi.org/10.1037/a0033264
King BM. The Influence of Social Desirability on Sexual Behavior Surveys: A Review. Arch Sex Behav. 2022;51(3):1495-1501. doi:10.1007/s10508-021-02197-0
King BM. Average-Size Erect Penis: Fiction, Fact, and the Need for Counseling. J Sex Marital Ther. 2021;47(1):80-89. doi:10.1080/0092623X.2020.1787279
King BM, Duncan LM, Clinkenbeard KM, Rutland MB, Ryan KM. Social Desirability and Young Men's Self-Reports of Penis Size. J Sex Marital Ther. 2019;45(5):452-455. doi:10.1080/0092623X.2018.1533905
Larson, R.B., 2019. Controlling social desirability bias. International Journal of Market Research, 61(5), pp.534-547.
Stodel, M. (2015). But What Will People Think?: Getting beyond Social Desirability Bias by Increasing Cognitive Load. International Journal of Market Research, 57(2), 313-322. https://doi.org/10.2501/IJMR-2015-024 (Original work published 2015)

Spreadsheet with Penis Length Data

Our online courses and programs:

Find us on social:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Introduction
(02:33) - Starbucks metric and episode themes
(07:17) - Men and women’s sampling frames
(09:24) - Kinsey and his studies
(14:59) - Statistics quiz on Kinsey penis data
(21:16) - Social desirability bias
(28:23) - Cognitive tricks to elicit honest survey answers
(34:16) - Condoms, honest penis lengths, and another stats quiz
(40:36) - Objective penis appraisers, measurement error, and reliability
(45:48) - Whose penises? Volunteer and selection bias
(49:33) - Mini-meta-analysis and the “answer”
(51:12) - Wrap-up and methodological morals

7 months ago

54 minutes

Normal Curves: Sexy Science, Serious Statistics

Sugar Sag: Is Your Diet Aging You?

Wrinkles and sagging skin—just normal aging, or can you blame your sweet tooth? We dive into “sugar sag,” exploring how sugar, processed foods, and even your crispy breakfast toast might be making you look older than if you’d said no to chocolate cake and yes to broccoli. Along the way, we encounter statistical adjustment, training and test data sets, what we call “references to nowhere,” plus some cadavers and collagen. Ever heard of an AGE reader? Find out how this tool might offer a sneak peek at your date’s age—and maybe even a clue about his… um… “performance.”

Statistical topics

Training and test sets
Statistical adjustment
Overfitting
Plagiarism
Proper citing practices
References to nowhere

Methodologic morals

“When you plagiarize, you steal the errors too.”
“Overdone statistical adjustment is like overdone photo filters–at a certain point it’s just laughable.”

Citations

Collagen turnover:

Verzijl N, DeGroot J, Thorpe SR, et al.Effect of Collagen Turnover on the Accumulation of Advanced Glycation End Products. JBC. 2000;275:39027-31.

Cadaver study:

Hamlin CR, Kohn RR, Luschin JH. Apparent Accelerated Aging of Human Collagen in Diabetes Mellitus. Diabetes. 1975; 24: 902–904.

AGE Reader

Studies of AGEs and diabetes and health:

Monnier VM, Cerami A. Nonenzymatic browning in vivo: possible process for aging of long-lived proteins. Science. 1981;211:491-3.
Brownlee M, Vlassara H, Cerami A. Nonenzymatic glycosylation and the pathogenesis of diabetic complications. Ann Intern Med. 1984;101:527-37.
Monnier VM, Vishwanath V, Frank KE, et al. Relation between Complications of Type I Diabetes Mellitus and Collagen-Linked Fluorescence. N Engl J Med. 1986;314:403-408.
Monnier VM, Sell DR, Abdul-Karim FW, et al. Collagen browning and cross-linking are increased in chronic experimental hyperglycemia. Relevance to diabetes and aging. Diabetes. 1988;37:867-72.
Monnier VM, Bautista O, Kenny D, et al. Skin collagen glycation, glycoxidation, and crosslinking are lower in subjects with long-term intensive versus conventional therapy of type 1 diabetes: relevance of glycated collagen products versus HbA1c as markers of diabetic complications. Diabetes 1999; 48: 870–80.
Genuth S, Sun W, Cleary P, et al. Glycation and carboxymethyllysine levels in skin collagen predict the risk of future 10-year progression of diabetic retinopathy and nephropathy in the diabetes control and complications trial and epidemiology of diabetes interventions and complications participants with type 1 diabetes. Diabetes. 2005;54:3103-11.
van Waateringe RP, Slagter SN, van Beek AP, et al. Skin autofluorescence, a non-invasive biomarker for advanced glycation end products, is associated with the metabolic syndrome and its individual components. Diabetol Metab Syndr. 2017;9:42.
Kouidrat Y, Zaitouni A, Amad A, et al. Skin autofluorescence (a marker for advanced glycation end products) and erectile dysfunction in diabetes. J Diabetes Complications. 2017;3:108-113.
Fujita N, Ishida M, Iwane T, et al. Association between Advanced Glycation End-Products, Carotenoids, and Severe Erectile Dysfunction. World J Mens Health. 2023;41:701-11.
Uruska A, Gandecka A, Araszkiewicz A, et al. Accumulation of advanced glycation end products in the skin is accelerated in relation to insulin resistance in people with Type 1 diabetes mellitus. Diabet Med. 2019;36:620-625.
Boersma HE, Smit AJ, Paterson AD, et al. Skin autofluorescence and cause-specific mortality in a population-based cohort. Sci Rep 2024;14:19967.

Review article with conflicts of interest:

Draelos ZD. Sugar Sag: What Is Skin Glycation and How Do You Combat It? J Drugs Dermatol. 2024; 23:s5-10.

Clinical study on AGE interrupter cream:

Draelos ZD, Yatskayer M, Raab S, Oresajo C. An evaluation of the effect of a topical product containing C-xyloside and blueberry extract on the appearance of type II diabetic skin. J Cosmet Dermatol. 2009;8:147-51.

The citation trail:

2023 review article: Zgutka K, Tkacz M, Tomasiak, et al. A Role for Advanced Glycation End Products in Molecular Ageing. Int J Mol Sci. 2023; 24: 9881. Sentence: “Interestingly, strict control of blood sugar for 4 months reduced the production of glycosylated collagen by 25%, and low-sugar food prepared by boiling could also reduce the production of AGEs [152].”
Reference 152 is a review article: Cao C, Xiao Z, Wu Y, et al. Diet and Skin Aging-From the Perspective of Food Nutrition. Nutrients. 2020;12:870. Sentence: “However, strict control of blood sugar for four months can reduce the production of glycosylated collagen by 25%, and low-sugar food prepared by boiling can also reduce the production of AGEs [93–95].”
Reference 93 is a review article: Nguyen HP, Katta R. Sugar sag: Glycation and the role of diet in aging skin. Skin Ther Lett. 2015; 20: 1–5. Sentence: “Tight glycemic control over a 4-month period can result in a reduction of glycated collagen formation by 25%.37,38”
Reference 94 and 38 is a review article: Draelos ZD. Aging skin: the role of diet: facts and controversies. Clin Dermatol. 2013;31:701-6. Sentence: “Tighter glycemic control can reduce glycated collagen by 25% in 4 months.” No citation given.
Reference 95 and 37 is a review article: Danby FW. Nutrition and aging skin: Sugar and glycation. Clin. Dermatol. 2010;28: 409–11. Sentence: “...tight glycemic control can drop glycated collagen formation by 25% in 4 months.” No citation given.
The origi...

7 months ago

1 hour 8 minutes

Normal Curves: Sexy Science, Serious Statistics

Hookworms: Can parasites improve your health?

What if you could treat your prediabetes with . . . worms? Regina and Kristin dive into a surprising early-phase clinical trial on hookworm therapy—that’s right, intentionally infecting yourself with parasitic worms—to treat metabolic conditions. They dig into the biological rationale (inflammation, abdominal fat, and gut immunology), the clever study design (hello, Tabasco sauce!), and the statistical chops behind this phase 1B trial (block randomization, missing data, and nonparametric hypothesis tests). Along the way, expect self-experimenting scientists, worm sex, poop analysis, and the world’s nerdiest aphrodisiac: a well-documented protocol.

Statistical topics

Randomized controlled trial (RCT)
Primary and secondary outcomes
Placebos, placebo effect, and nocebo effect
Block randomization
Sample size
Double-blinding
Missing data protocols
Reproducible research
Nonparametric hypothesis testing
Kruskal-Wallis test

Methodological morals

“Walk before you can run. Invest in simple but high-quality Phase I clinical trials.”
“When faced with small samples, you better rank and sum, baby.”

References

Pierce DR, McDonald M, Merone L, et al. Effect of experimental hookworm infection on insulin resistance in people at risk of type 2 diabetes. Nat Commun. 2023;14(1):4503. doi:10.1038/s41467-023-40263-4
Nuzzo, R. What Happens When Scientists Experiment on Themselves? Reader’s Digest. Updated April 16, 2016.
Ethics of self-experimentation
Helminthic Therapy Wiki
Pritchard D. Worming your way to good health [video online]. TEDx Talks. Published November 19, 2013. https://www.youtube.com/watch?v=sm5DcdZbnGA
Chapman PR, Giacomin P, Loukas A, McCarthy JS. Experimental human hookworm infection: a narrative historical review. PLoS Negl Trop Dis. 2021;15(12):e0009908. doi:10.1371/journal.pntd.0009908
Sobotková K, Parker W, Levá J, Růžková J, Lukeš J, Jirků Pomajbíková K. Helminth Therapy - From the Parasite Perspective. Trends Parasitol. 2019;35(7):501-515. doi:10.1016/j.pt.2019.04.009
Zatterale F, Longo M, Naderi J, et al. Chronic Adipose Tissue Inflammation Linking Obesity to Insulin Resistance and Type 2 Diabetes. Front Physiol. 2020;10:1607. Published 2020 Jan 29. doi:10.3389/fphys.2019.01607

Kristin and Regina’s online courses

Demystifying Data: A Modern Approach to Statistical Understanding

Epidemiology and Clinical Research Graduate Certificate Program

Program we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(02:44) - What happens when scientists experiment on themselves
(06:56) - Mail-order DIY helminthic therapy
(09:26) - Hookworm biology
(15:53) - Inflammation, abdominal fat, immune system, and hookworms
(21:29) - Hookworm therapy clinical trial design
(26:00) - Clinical trial phases deep dive
(31:24) - Interesting placebos (sham surgeries and psychedelics)
(37:33) - Excitement over hookworm trial open data and data protocols
(44:45) - Hookworm trial results
(48:48) - Mood and well-being with hookworms
(53:26) - Effects of hookworms on weight
(56:09) - Nonparametric tests and how they work
(01:02:56) - What the participants did after the study
(01:04:53) - Wrap-up

8 months ago

1 hour 8 minutes

Normal Curves: Sexy Science, Serious Statistics

Alcohol: Are happy hours good for your heart?

Does a daily glass of wine really keep the cardiologist away? It’s a claim we’ve all heard: light to moderate drinking is good for your heart. But is it science or just a convenient excuse for happy hour? In this episode, we dive into the history behind this claim, discuss the challenges of observational studies and statistical adjustment, and explore attempts at randomized trials and natural experiments to get to the bottom of this boozy debate. Grab your drink—or maybe don’t—and join us!

Statistical topics

Statistical Adjustment
Regression
Residual and Unmeasured Confounding
Randomized Trials
Multiple Testing
Outcome Switching
Mendelian Randomization

Methodological morals
“Statistical adjustment cannot erase all confounding.”

“When you can’t experiment on people, let Nature experiment on people.”

Citations

Alcohol and Drinking. Gallup Poll. 2024.
Canada’s Guidance on Alcohol and Health. Canadian Centre on Substance Use and Addiction. 2023.
U.S. Department of Agriculture and U.S. Department of Health and Human Services. 2020 – 2025 Dietary Guidelines for Americans. 2020.
U.S. Department of Agriculture and U.S. Department of Health and Human Services. Alcohol and Cancer Risk: The US Surgeon General's Advisory. 2025.
Mezue K, Osborne MT, Abohashem S, et al. Reduced stress-related neural network activity mediates the effect of alcohol on cardiovascular risk. JACC. 2023;81:2315-25.
McPhillips D, Goodman B. Small amounts of alcohol may turn down stress in the brain, benefiting the heart, new study finds. CNN. June 12, 2023.
Friedman GD, Klatsky AL. Is alcohol good for your health? NEJM. 1993;329:1882-3.
Sainani K. Alcohol and Weight. Allure. July 14, 2010.
Wang LU, Lee IM, Manson JE, et al. Alcohol consumption, weight gain, and risk of becoming overweight in middle-aged and older women. Arch Intern Med. 2010;170:453-61.
Sainani K. Drinking and Weight. Allure. Oct 31, 2008.
Tolstrup JS, Halkjær J, Heitmann BL, et al. Alcohol drinking frequency in relation to subsequent changes in waist circumference. Am J Clin Nutr. 2008;87:957-63.
Rabin RC. Major Study on Drinking Will be Shut Down. New York Times. June 15, 2018.
Mitchell G, Lesch M, McCambridge J. Alcohol industry involvement in the moderate alcohol and cardiovascular health trial. AJPH. 2020;110:485-8.
Gepner Y, Golan R, Harman-Boehm I, et al. Effects of Initiating Moderate Alcohol Intake on Cardiometabolic Risk in Adults With Type 2 Diabetes: A 2-Year Randomized, Controlled Trial. Ann Intern Med. 2015;163:569-79.
Slade E, Drysdale H, Goldacre B, et al. Discrepancies between prespecified and reported outcomes. Ann Intern Med. 2016;164:374.
Biddinger KJ, Emdin CA, Haas ME, et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Netw Open. 2022;5:e223849.

Page with more details on the CASCADE trial

Kristin and Regina’s online courses:

Program that we teach in:

Epidemiology and Clinical Research Graduate Certificate Program

Find us on:

Kristin - LinkedIn & Twitter/X

Regina - LinkedIn & ReginaNuzzo.com

(00:00) - Introduction
(03:00) - Drinking habits in America
(04:13) - New Canadian drinking guidelines
(07:51) - Definition of light-to-moderate drinking
(08:43) - Risks and benefits of light-to-moderate drinking
(11:37) - History of the heart health claim
(18:34) - Problems with observational studies
(22:40) - Statistical adjustment
(25:39) - Residual and unmeasured confounding
(31:19) - Overconfidence in observational studies
(35:16) - Randomized trials of alcohol
(36:32) - Canceled NIH randomized trial of alcohol
(41:42) - The CASCADE randomized trial of wine
(43:18) - The problem of multiple testing
(47:56) - Outcome switching
(49:32) - Mendelian randomization
(59:04) - Mendelian randomization studies of alcohol and heart disease
(01:03:09) - Wrap-up

8 months ago

1 hour 5 minutes

Normal Curves: Sexy Science, Serious Statistics

The Red Dress Effect: Are women in red sexier?

Wear red and drive men wild with lust – or so says scientific research on color’s role in human mating. But can a simple color swap really boost a woman’s hotness score? In this episode, we delve into the evidence behind the Red Dress Effect, from a controversial first study in college men to what the latest research says about who this trick might work for (and who it might not). Along the way we encounter red monkey butts, old-Internet websites, the Winner’s Curse in scientific research, adversarial collaborations, and why size (ahem, sample size) really does matter.

Statistical topics

Reproducibility crisis in psychology
Sample size
Selection bias
Winner’s curse
Cohen’s d standardized effect size
Adversarial collaboration
Meta-analysis
Preregistration
Publication bias
Statistical moderators

Methodological morals

“The smaller the sample, the flashier the result, the less you should trust it.”

“Good scientists learn from their statistical mistakes and fix them.”

References

Clarke, G. How to Be Sexy: 7 Weird (But True) Rules of Attraction. Allure. January 23, 2013.
Nuzzo, R. Vying for a soul mate? Psych out the competition with science. Los Angeles Times. December 8, 2008.
Vying for a Soul Mate on the Today Show, December 30, 2008.
Elliot, A.J. and Niesta, D. Romantic red: red enhances men's attraction to women. Journal of personality and social psychology. 2008; 95: 1150 – 1164.
Lehrer, J. The Truth Wears Off. The New Yorker, December 5, 2010.
MacMahon, B., Yen, S., Trichopoulos, D., Warren, K. and Nardi, G. Coffee and cancer of the pancreas. New England Journal of Medicine. 1981; 304: 630-633.
Ioannidis, J.P. Why most published research findings are false. PLoS medicine. 2005; 2(8), e124
Lehmann, G.K., Elliot, A.J. and Calin-Jageman, R.J. Meta-analysis of the effect of red on perceived attractiveness. Evolutionary Psychology. 2018; 16: 1474704918802412.
Pazda, A.D., Thorstenson, C.A. and Elliot, A.J. The effect of red on attractiveness for highly attractive women. Curr Psychol. 2023; 42: 8066–8073

Kristin and Regina’s online courses:

Demystifying Data: A Modern Approach to Statistical Understanding